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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Developing Code for Data Processing using Python and SQL	22%	- Implement complex data processing logic - Use Databricks-specific libraries and APIs - Write efficient and maintainable code
Data Transformation, Cleansing, and Quality	10%	- Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management
Ensuring Data Security and Compliance	10%	- Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit
Data Governance	7%	- Use Unity Catalog for governance - Enforce data policies and standards - Manage data assets and metadata
Data Sharing and Federation	5%	- Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing
Data Modelling	6%	- Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture
Monitoring and Alerting	10%	- Track data lineage and metrics - Monitor pipeline performance and health - Set up alerts and notifications
Debugging and Deploying	10%	- Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs
Cost & Performance Optimisation	13%	- Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources
Data Ingestion & Acquisition	7%	- Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題 (Q26-Q31):

問題 #26
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Use &sh install in a notebook cell
B. Run source env/bin/activate in a notebook setup script
C. Install libraries from PyPi using the cluster UI
D. Use &Pip install in a notebook cell

答案：C

解題說明：
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.

問題 #27
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

A. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
B. spark.sql("VACUUM my_table RETAIN 15 HOURS")
C. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")
D. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"

答案：C

解題說明：
The deleted file retention period in Delta Lake is controlled by the table property delta.deletedFileRetentionDuration. Setting this property via ALTER TABLE ensures the retention policy is persistently enforced at the table level, extending deleted file retention to 15 days in compliance with organizational requirements.

問題 #28
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Instruct the marketing team to download results as a CSV and email them to the sales organization.
B. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
C. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
D. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
E. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.

答案：B

解題說明：
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.

問題 #29
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

A. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
B. Parse the Delta Lake transaction log to identify all newly written data files.
C. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
D. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.

答案：C

解題說明：
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.

問題 #30
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
B. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
C. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
D. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
E. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.

答案：B

解題說明：
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

問題 #31
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